International Journal of Power Electronics and Drive Systems (IJPEDS)
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In-depth evaluation and enhancement of a PV-wind combined system: A case study at the Engineering Faculty of Wahid Hasyim University
Get PDFEnergy sustainability is crucial for mitigating climate change and reducing dependence on fossil fuels. This research evaluates a hybrid renewable energy system combining photovoltaic (PV) technology and wind turbines to meet the electricity demand of Wahid Hasyim University's Faculty of Engineering, totalling 555,000 VA. Using HOMER Pro software, the study identifies the optimal configuration based on technical, economic, and environmental aspects. The hybrid system integrating PV, wind turbines, batteries, and converters achieves the lowest Net Present Cost (NPC) of 0.0185/kWh, outperforming grid-only systems. Environmentally, the system significantly reduces carbon dioxide (CO2) emissions, from 559,226 kg/yr in conventional systems to 62,452 kg/yr. Solar energy contributes 56% of electricity generation, leveraging stable solar radiation of 4.28–5.54 kWh/m²/day. Additionally, an annual surplus of 156,350 kWh can be sold back to the grid, enhancing operational efficiency. This study demonstrates that hybrid renewable energy systems deliver long-term cost efficiency and significantly mitigate climate impacts. It provides a sustainable energy model for campuses in Indonesia and worldwide, particularly in regions with abundant solar resources
Permanent magnet generator performance comparison under different topologies and capacities
Get PDFThis paper compares the magnetic, electrical, and mechanical characteristics of two permanent magnet generator topologies: single-gap axial flux and single-gap inner rotor radial flux. The study aims to identify how the key parameters fluctuate at each power capacity and investigate the trends in their values as power changes. The power capacities observed are 300 W, 600 W, 900 W, 1200 W, and 1500 W. Simulations used with the help of Ansys Maxwell software to obtain: i) magnetic characteristics without load, including air gap flux density, flux linkage, and induced voltage, ii) electrical performance, consisting of armature current, terminal voltage, voltage regulation, total harmonic distortion, core loss and output power, and iii) mechanical performance, including shaft torque and cogging torque. The last step compares the power density of both topologies. The simulation results show that the axial flux permanent magnet generator (AFPMG) has better air gap flux density, voltage regulation, total harmonic distortion (THD), efficiency, electromagnetic torque, and power density characteristics. Meanwhile, the radial flux permanent magnet generator (RFPMG) is superior in induced voltage and output power. These results conclude that, in general, AFPMG is exceptional from a technical point of view and is more economical when applied to hydro or wind energy systems
Softplus function trained artificial neural network based maximum power point tracking
Get PDFTo optimize the electrical output of a photovoltaic system, maximum power point tracking (MPPT) methods are commonly employed. These techniques work by operating the photovoltaic system at its maximum power point (MPP), which varies based on environmental factors like solar irradiance and ambient temperature, thereby ensuring optimal power transfer between the photovoltaic system and the load. In this paper, an artificial neural network (ANN) is selected as an MPPT technique. The main contribution of the work is to introduce a softplus function trained artificial neural network-based maximum point tracking (SP-ANN MPPT). The proposed method is then compared with a sigmoid function trained artificial neural network-based maximum point tracking (SM-ANN MPPT). The simulation and experimental results show that SP-ANN MPPT is able to track high power than SM-ANN MPPT in different conditions
Estimator-based single phase second order variable structure controller for the pitch control of a variable speed wind turbine
Get PDFA novel single phase second order variable structure controller (SPSOVSC) based on estimated variables and output information only is presented for the variable speed wind turbine (VSWT) system. In contrast with a recent method, the output feedback and second order sliding mode control techniques are deliberated for the SPSOVSC design in the VSWT. The selection of an integral single-phase sliding surface is established such that the reaching phase required in the basic variable structure control (BVSC) scheme is removed since the plant’s state trajectories always begin from the sliding surface. In addition, appropriate stability constraints by Lyapunov based novel linear matrix inequality (LMI) technique are acquired to guarantee the entire VSWT plant’s steadiness. Using the proposed techniques, the SPSOVSC is developed to modify BVSC to advance the performance of VSWT plant in terms of overshoot and settling time. The results show the new scheme is highly robust in sliding variable's fast convergence to zero asymptotically. It is obvious that the robustness of the proposed controller in terms of steadiness and usefulness of the scheme
Comparing multi-control algorithms for complex nonlinear system: An embedded programmable logic control applications
Get PDFThis paper examines the impact of multiple control algorithms, such as genetic algorithm (GA), artificial neural network (ANN), and proportional integral derivative (PID), on programmable logic controller (PLC) performance during a nonlinear thermodynamic process. The ANN was trained with data that modeled the thermodynamic process and then generated the control algorithm. GA was improved by applying the counter-premature algorithm (CPA) to address issues of pre-mature convergence, while the PID presents the current algorithm used to optimize the PLC in the existing testbed. Experimental evaluation of these models against the process set-points showed that all the algorithms were able to reject disturbance and follow the reference set points under different step changes, but each algorithm experienced different internal behaviors while trying to reject disturbance. Lastly, the result showed that while the improved GA was better than the PID, with a recorded slight overshoot due to the uncertainties of the thermodynamic process, the ANN achieved better control performance in terms of system stability than the other counterpart algorithms
Sizing optimization of a standalone PV/wind hybrid energy system with battery storage using a genetic algorithm
Get PDFRenewable energy sources, such as wind and solar, are clean and widely available, they have significant advantages over conventional power. However, the climate has an inherent influence on their production. Due to growing energy costs and decreasing solar and wind turbine prices, the use of PV/wind hybrid energy systems has grown in popularity. Determining the ideal number of PV panels and wind turbines required is essential to minimize costs and ensure the continuous production of energy to fulfill the intended demand before building a renewable energy generating facility. The goal of this research is to identify the optimal design for a hybrid PV/wind system that includes battery storage for standalone uses. The suggested analysis uses the low power supply probability (LPSP) as a guiding metric and a genetic algorithm (GA) to optimize costs while reliably satisfying load requirements. With this technology, the ideal quantity of PV modules and wind turbines may be precisely determined at the lowest possible cost. The outcomes show that the hybrid systems have undergone effective optimization
Dual-aware EV charging scheduling with traffic integration
Get PDFElectric vehicle adoption is a trend in many countries, and the demand for charging station infrastructure is at a rapid pace. The placement of charging stations is the key research topic of many researchers, but charging scheduling is also a problem that is going to rise in the near future. The proper charger utilization, maintaining coordination between charging stations, and satisfying users' demands are some of the key challenges. The traffic pattern is uncertain, coordination of distances between charging stations and users is done by Euclidean distance. The traffic-aware fair charging scheduling (TAFCS) strategy is proposed, which will have a balance on charger utilization and user prioritization, and keep the fairness by equal distribution of electric vehicles among all the charging stations having a centralized charging system monitored by an aggregator. The distribution of the traffic pattern of electric vehicles is performed by Monte Carlo simulation. The proposed system is tested on the IEEE 33 bus standard system using the predefined voltage limits of each bus and limiting power loss to lessen its burden. The discharging process of 50 electric vehicles (V2G) is performed by optimal placement by obtaining the weakest buses, which makes it an intelligent distribution system. This proposed charging framework is validated on MATLAB R2020a
Composite least mean fourth algorithm (CLMF) based dynamic voltage restorer for enhancement of power quality
Get PDFThis paper introduces the composite least mean fourth control algorithm (CLMF) with a dynamic voltage restorer (DVR) to address power quality problems linked to voltage at the source side and supply clean voltage to the distribution network's sensitive loads. The performance of the two least mean fourth adaptive filters combined convexly by this control technique is better than that of the filters working independently. When comparing the suggested control to conventional synchronous reference frame-based vector control, phase-locked loops, abc to dq transformations, and dq to abc transformations are all practically eliminated. When compared to standard least mean square (LMS) and least mean fourth (LMF) control approaches, the proposed CLMF's features—simple computation, ease of implementation, reduced settling time, and increased reliability—show that the suggested controller is more efficient. The proposed CLMF controller excels in terms of rise time, 0.082 sec., and less settling time, 0.092 sec., respectively, with a peak overshoot of 2.33% compared with the aforementioned control algorithms. Different voltage-related PQ issues have been corrected successfully by the proposed CLMF. Through simulation using MATLAB/Simulink, system performance has been verified
LQG-based optimal control approach of an electronic throttle valves using DC servo system
Get PDFA direct current (DC) motor is used for automotive electronic throttle valves (AETV) to adjust incoming air into the engine’s combustion system, which has many advantages such as smooth, fast response, and simplicity. However, high-accuracy tracking control for AETV faces various obstacles because of the nonlinear features, hard identification, and noise. In this paper, a model of the AETV with four states in the form of a state space is developed. Then, a Kalman filter is formulated to eliminate the impact of measurement noise. The Kalman filter gain is obtained via the solve the linear quadratic gaussian (LQG) equation. Next, the optimal control based linear quadratic regulation (LQR) and Kalman filter are presented in which the control gain is constructed by the Riccati equation with the assistance of MATLAB/Simulink software. Finally, simulation studies are conducted to demonstrate the efficiency of the suggested method for the AETV system with other control strategies
Improved hybrid DTC technology for eCAR 4-wheels drive
Get PDFThis article deals with the design of a hybrid controller (HyC). It combines fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS). It is combined with direct torque control (DTC). This HyC-DTC combination is designed to improve the technical performance of a 04-wheel drive electric vehicle (EV). A stress test is identically applied to the DTC combined with the FL (FDTC) and to the HyC-DTC in order to certify the suitability of this new control following a cross-validation. This is based on dynamic stability criteria (overshoot, rise time, accuracy), analysis of torque and flux oscillations, and the EV's robustness symbol. The EV's magnetic quantities are managed by a master-slave module (VMSC). Simulations are carried out using MATLAB/Simulink software. The HyC-DTC achieves near-zero accuracy like the FDTC, with overshoot around 0.2% less than the FDTC, and torque oscillation amplitude around 4 times less than the FDTC. However, its rise time is 0.045% greater than that of the FDTC. It is therefore slower, but more precise and suitable for EV transmission systems in terms of safety and comfort